{"id":"https://openalex.org/W4312352439","doi":"https://doi.org/10.1109/ijcnn55064.2022.9892415","title":"Light-Weight Branch-Shared Multi-View Convolutional Neural Networks Crowd Counting","display_name":"Light-Weight Branch-Shared Multi-View Convolutional Neural Networks Crowd Counting","publication_year":2022,"publication_date":"2022-07-18","ids":{"openalex":"https://openalex.org/W4312352439","doi":"https://doi.org/10.1109/ijcnn55064.2022.9892415"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn55064.2022.9892415","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn55064.2022.9892415","pdf_url":null,"source":{"id":"https://openalex.org/S4363607707","display_name":"2022 International Joint Conference on Neural Networks (IJCNN)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100319205","display_name":"Yonghui Wang","orcid":"https://orcid.org/0000-0003-4334-0921"},"institutions":[{"id":"https://openalex.org/I83714178","display_name":"Shenyang Jianzhu University","ror":"https://ror.org/01zr73v18","country_code":"CN","type":"education","lineage":["https://openalex.org/I83714178"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yonghui Wang","raw_affiliation_strings":["Shenyang Jianzhu University,Shenyang,China","Shenyang Jianzhu University, Shenyang, China"],"affiliations":[{"raw_affiliation_string":"Shenyang Jianzhu University,Shenyang,China","institution_ids":["https://openalex.org/I83714178"]},{"raw_affiliation_string":"Shenyang Jianzhu University, Shenyang, China","institution_ids":["https://openalex.org/I83714178"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100421480","display_name":"Yang Li","orcid":"https://orcid.org/0000-0002-3006-7420"},"institutions":[{"id":"https://openalex.org/I83714178","display_name":"Shenyang Jianzhu University","ror":"https://ror.org/01zr73v18","country_code":"CN","type":"education","lineage":["https://openalex.org/I83714178"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang Li","raw_affiliation_strings":["Shenyang Jianzhu University,Shenyang,China","Shenyang Jianzhu University, Shenyang, China"],"affiliations":[{"raw_affiliation_string":"Shenyang Jianzhu University,Shenyang,China","institution_ids":["https://openalex.org/I83714178"]},{"raw_affiliation_string":"Shenyang Jianzhu University, Shenyang, China","institution_ids":["https://openalex.org/I83714178"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5062499743","display_name":"Ke Tu","orcid":"https://orcid.org/0009-0009-4922-1684"},"institutions":[{"id":"https://openalex.org/I83714178","display_name":"Shenyang Jianzhu University","ror":"https://ror.org/01zr73v18","country_code":"CN","type":"education","lineage":["https://openalex.org/I83714178"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ke Tu","raw_affiliation_strings":["Shenyang Jianzhu University,Shenyang,China","Shenyang Jianzhu University, Shenyang, China"],"affiliations":[{"raw_affiliation_string":"Shenyang Jianzhu University,Shenyang,China","institution_ids":["https://openalex.org/I83714178"]},{"raw_affiliation_string":"Shenyang Jianzhu University, Shenyang, China","institution_ids":["https://openalex.org/I83714178"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100319205"],"corresponding_institution_ids":["https://openalex.org/I83714178"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.13681734,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"23","issue":null,"first_page":"01","last_page":"07"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9968000054359436,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12597","display_name":"Fire Detection and Safety Systems","score":0.973800003528595,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.8012106418609619},{"id":"https://openalex.org/keywords/crowds","display_name":"Crowds","score":0.7897765040397644},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7512809038162231},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7335094809532166},{"id":"https://openalex.org/keywords/fuse","display_name":"Fuse (electrical)","score":0.6636050939559937},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6282221078872681},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5373818278312683},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.4885162115097046},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.46718457341194153},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.46423402428627014},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.43823644518852234},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4242444932460785},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.41341403126716614},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1306203305721283},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.11322978138923645},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.08207458257675171}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.8012106418609619},{"id":"https://openalex.org/C2777852691","wikidata":"https://www.wikidata.org/wiki/Q13430821","display_name":"Crowds","level":2,"score":0.7897765040397644},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7512809038162231},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7335094809532166},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.6636050939559937},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6282221078872681},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5373818278312683},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.4885162115097046},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46718457341194153},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.46423402428627014},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.43823644518852234},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4242444932460785},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.41341403126716614},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1306203305721283},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.11322978138923645},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.08207458257675171},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn55064.2022.9892415","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn55064.2022.9892415","pdf_url":null,"source":{"id":"https://openalex.org/S4363607707","display_name":"2022 International Joint Conference on Neural Networks (IJCNN)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8299999833106995,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W603908379","https://openalex.org/W1910776219","https://openalex.org/W2011285823","https://openalex.org/W2036549113","https://openalex.org/W2116022929","https://openalex.org/W2133665775","https://openalex.org/W2145983039","https://openalex.org/W2463631526","https://openalex.org/W2729018917","https://openalex.org/W2798618325","https://openalex.org/W2895051362","https://openalex.org/W2955171701","https://openalex.org/W2962832028","https://openalex.org/W2963035940","https://openalex.org/W2969620138","https://openalex.org/W2982007926","https://openalex.org/W2997135920","https://openalex.org/W3007175960","https://openalex.org/W3027606690","https://openalex.org/W3120048558","https://openalex.org/W3175657805","https://openalex.org/W3176047859","https://openalex.org/W3184439416","https://openalex.org/W4205830817","https://openalex.org/W6618372016","https://openalex.org/W6681368121","https://openalex.org/W6754756387","https://openalex.org/W6763278908","https://openalex.org/W6767206290","https://openalex.org/W6798838024","https://openalex.org/W6806606854"],"related_works":["https://openalex.org/W4240200267","https://openalex.org/W1511510665","https://openalex.org/W2154955495","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3167935049","https://openalex.org/W3029198973"],"abstract_inverted_index":{"Crowd":[0],"counting":[1,21,47,66],"plays":[2],"an":[3],"important":[4],"role":[5],"in":[6,40,59,137],"event":[7],"planning,":[8],"video":[9],"surveillance,":[10],"and":[11,77,86,96,168],"other":[12],"fields.":[13],"At":[14],"present,":[15],"the":[16,28,55,60,70,88,104,112,134,143],"development":[17],"of":[18,30,34,57,106],"single-view":[19],"crowd":[20,46,65],"is":[22,37],"relatively":[23],"mature,":[24],"but":[25],"due":[26],"to":[27,53,116,140,153],"limitation":[29],"a":[31,94,154],"single":[32],"field":[33],"view,":[35],"it":[36],"not":[38],"suitable":[39],"some":[41],"dense":[42],"occluded":[43],"scenes.":[44],"Multi-view":[45],"uses":[48,111],"images":[49,122],"from":[50,123],"multiple":[51],"views":[52],"estimate":[54],"number":[56,105],"crowds":[58],"current":[61],"scene.":[62],"Most":[63],"multi-view":[64],"methods":[67],"based":[68],"on":[69,162],"deep":[71],"convolutional":[72,98],"neural":[73,99],"networks":[74,100],"use":[75],"independent":[76],"identical":[78],"view":[79,114],"branches,":[80],"which":[81,102],"bring":[82],"massive":[83],"redundant":[84],"features":[85],"increase":[87],"model's":[89],"complexity.":[90],"This":[91,109],"paper":[92],"proposes":[93],"light-weight":[95],"branch-shared":[97],"method,":[101],"decreases":[103],"learnable":[107],"parameters.":[108],"method":[110,175],"same":[113,135],"branch":[115],"extract":[117],"multi-scale":[118],"feature":[119,128,145],"maps":[120,129,146],"with":[121,170],"different":[124,149],"views.":[125],"The":[126],"camera-view":[127],"will":[130],"be":[131],"projected":[132],"into":[133],"plane":[136],"world":[138],"space":[139],"fuse,":[141],"then":[142],"scene-level":[144,155],"extracted":[147],"at":[148],"scales":[150],"are":[151,160],"regressed":[152],"density":[156],"map.":[157],"Extensive":[158],"experiments":[159],"conducted":[161],"two":[163],"public":[164],"datasets":[165],"(PETS2009,":[166],"CityStreet),":[167],"compared":[169],"five":[171],"existing":[172],"methods,":[173],"this":[174],"can":[176],"achieve":[177],"better":[178],"performance.":[179]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}